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1. Introduction The self-stablization of subtropical deserts through an albedo-dominated biogeophysical feedback as proposed by Charney (1975) raised the possibility that biosphere–atmosphere interactions may give rise to the existence of multiple equilibria in the earth's climate system. Specifically, the increase of albedo due to natural or manmade desertification causes a radiative cooling at the land surface that induces subsidence of the air, thus suppressing precipitation. This
1. Introduction The self-stablization of subtropical deserts through an albedo-dominated biogeophysical feedback as proposed by Charney (1975) raised the possibility that biosphere–atmosphere interactions may give rise to the existence of multiple equilibria in the earth's climate system. Specifically, the increase of albedo due to natural or manmade desertification causes a radiative cooling at the land surface that induces subsidence of the air, thus suppressing precipitation. This
surface and the atmosphere interact in complex nonlinear ways ( Delworth and Manabe 1993 ; Koster et al. 2000 ). The availability of long-term datasets of the state of the land surface—and particularly the biosphere—from satellite records creates new opportunities for quantifying land surface–atmosphere interactions. Phenology investigates trends in the timing of recurrent seasonal events. A statistical framework for the analysis of Advanced Very High Resolution Radiometer (AVHRR) time series data is
surface and the atmosphere interact in complex nonlinear ways ( Delworth and Manabe 1993 ; Koster et al. 2000 ). The availability of long-term datasets of the state of the land surface—and particularly the biosphere—from satellite records creates new opportunities for quantifying land surface–atmosphere interactions. Phenology investigates trends in the timing of recurrent seasonal events. A statistical framework for the analysis of Advanced Very High Resolution Radiometer (AVHRR) time series data is
1. Introduction The interactions at the interface between the land surface and the atmosphere are modeled using the surface component of climate models, which is known as a soil–vegetation–atmosphere transfer scheme. The need to improve the representation of land surface biophysical processes, terrestrial carbon flux, and vegetation dynamic in atmospheric general circulation models and regional models, mainly for climatic change studies, has stimulated the development of sophisticated surface
1. Introduction The interactions at the interface between the land surface and the atmosphere are modeled using the surface component of climate models, which is known as a soil–vegetation–atmosphere transfer scheme. The need to improve the representation of land surface biophysical processes, terrestrial carbon flux, and vegetation dynamic in atmospheric general circulation models and regional models, mainly for climatic change studies, has stimulated the development of sophisticated surface
biosphere–atmosphere statistical–dynamical model (SDM) with a detailed parameterization of the radiative processes is used. This kind of model is essentially mechanistic, being directed toward understanding the dependence of a particular mechanism on the other parameters of the problem. In GCMs, since many mechanisms are included simultaneously, the cause and effect relationship is not always possible to trace. Thus, an SDM is better suited for the present study. This paper differs from the previous
biosphere–atmosphere statistical–dynamical model (SDM) with a detailed parameterization of the radiative processes is used. This kind of model is essentially mechanistic, being directed toward understanding the dependence of a particular mechanism on the other parameters of the problem. In GCMs, since many mechanisms are included simultaneously, the cause and effect relationship is not always possible to trace. Thus, an SDM is better suited for the present study. This paper differs from the previous
. BCC-AVIM is Beijing Climate Center Atmosphere and Vegetation Interaction Model; CTEM is Canadian Terrestrial Ecosystem Model; MATSIRO is Minimal Advanced Treatments of Surface Interaction and Runoff; SEIB-DGVM is Spatially Explicit Individual-Based Dynamic Global Vegetation Model; and HAL is Hydrology, Atmosphere, and Land Surface Model from MRI-ESM1; additional expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList . By sharing identical land surface model and
. BCC-AVIM is Beijing Climate Center Atmosphere and Vegetation Interaction Model; CTEM is Canadian Terrestrial Ecosystem Model; MATSIRO is Minimal Advanced Treatments of Surface Interaction and Runoff; SEIB-DGVM is Spatially Explicit Individual-Based Dynamic Global Vegetation Model; and HAL is Hydrology, Atmosphere, and Land Surface Model from MRI-ESM1; additional expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList . By sharing identical land surface model and
forest degradation on Borneo ( Kumagai et al. 2013 ). To quantify how the LULCC affects the land surface processes, the surface-induced mesoscale circulation, the atmospheric interaction between land and sea, and the resulting regional precipitation over the island, we used the WRF Model with an appropriate land surface model to describe the influence of changes in vegetation status on the atmosphere (Noah LSM; Mitchell 2005 ). Since the diurnal cycle of precipitation is a dominant feature of the
forest degradation on Borneo ( Kumagai et al. 2013 ). To quantify how the LULCC affects the land surface processes, the surface-induced mesoscale circulation, the atmospheric interaction between land and sea, and the resulting regional precipitation over the island, we used the WRF Model with an appropriate land surface model to describe the influence of changes in vegetation status on the atmosphere (Noah LSM; Mitchell 2005 ). Since the diurnal cycle of precipitation is a dominant feature of the
1. Introduction Interannual variability of the global carbon cycle is closely related to El Niño–Southern Oscillation (ENSO), which is characterized by anomalous sea surface warming and cooling in the eastern and central Pacific ( Bacastow 1976 ; Keeling and Revelle 1985 ; Braswell et al. 1997 ; Rayner and Law 1999 ). ENSO is a conspicuous component of ocean–atmosphere interactions in the equatorial climate system, and has an enormous influence on the ecosystem and global carbon cycle
1. Introduction Interannual variability of the global carbon cycle is closely related to El Niño–Southern Oscillation (ENSO), which is characterized by anomalous sea surface warming and cooling in the eastern and central Pacific ( Bacastow 1976 ; Keeling and Revelle 1985 ; Braswell et al. 1997 ; Rayner and Law 1999 ). ENSO is a conspicuous component of ocean–atmosphere interactions in the equatorial climate system, and has an enormous influence on the ecosystem and global carbon cycle
– 1340 , doi: 10.1016/j.agrformet.2008.03.012 . 10.1016/j.agrformet.2008.03.012 Goodin , D. G. , J. M. S. Hutchinson , R. L. Vanderlip , and M. C. Knapp , 1999 : Estimating solar irradiance for crop modeling using daily air temperature data . Agron. J. , 91 , 845 – 851 , doi: 10.2134/agronj1999.915845x . 10.2134/agronj1999.915845x Grote , R. , E. Lehmann , C. Bruemmer , N. Brueggemann , J. Szarzynski , and H. Kunstmann , 2009 : Modelling and observation of biosphere–atmosphere
– 1340 , doi: 10.1016/j.agrformet.2008.03.012 . 10.1016/j.agrformet.2008.03.012 Goodin , D. G. , J. M. S. Hutchinson , R. L. Vanderlip , and M. C. Knapp , 1999 : Estimating solar irradiance for crop modeling using daily air temperature data . Agron. J. , 91 , 845 – 851 , doi: 10.2134/agronj1999.915845x . 10.2134/agronj1999.915845x Grote , R. , E. Lehmann , C. Bruemmer , N. Brueggemann , J. Szarzynski , and H. Kunstmann , 2009 : Modelling and observation of biosphere–atmosphere
) dataset (an indicator of green leaf area) and antecedent precipitation levels, particularly in temperate and tropical grasslands. A modeling framework is a natural venue for studying the connections between carbon and water. Wang and Eltahir (2000) , using a simple coupled biosphere–atmosphere model, showed how the interaction between vegetation and precipitation can lead to multiple equilibria for vegetation state. Zeng et al. (1999) showed, again with a simple coupled model, how vegetation
) dataset (an indicator of green leaf area) and antecedent precipitation levels, particularly in temperate and tropical grasslands. A modeling framework is a natural venue for studying the connections between carbon and water. Wang and Eltahir (2000) , using a simple coupled biosphere–atmosphere model, showed how the interaction between vegetation and precipitation can lead to multiple equilibria for vegetation state. Zeng et al. (1999) showed, again with a simple coupled model, how vegetation
biosphere–atmosphere interaction, also varies substantially in the space–time domain ( Wyrtki et al. 1976 ; Bonan et al. 1995 ). As extensively discussed in previous modeling and observational studies, the driving force for this interchange insofar as heat and water are concerned, is the available energy which is linked to water vapor pressure and the temperature gradient (e.g., Monteith 1965 ; Pielke et al. 2002 ). This complex interaction is connected to the thermal and dynamic behavior of the
biosphere–atmosphere interaction, also varies substantially in the space–time domain ( Wyrtki et al. 1976 ; Bonan et al. 1995 ). As extensively discussed in previous modeling and observational studies, the driving force for this interchange insofar as heat and water are concerned, is the available energy which is linked to water vapor pressure and the temperature gradient (e.g., Monteith 1965 ; Pielke et al. 2002 ). This complex interaction is connected to the thermal and dynamic behavior of the
